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Connection

Yonglin Pu to Lung Neoplasms

This is a "connection" page, showing publications Yonglin Pu has written about Lung Neoplasms.
Connection Strength

3.995
  1. Whole-body tumor segmentation from PET/CT images using a two-stage cascaded neural network with camouflaged object detection mechanisms. Med Phys. 2023 Oct; 50(10):6151-6162.
    View in: PubMed
    Score: 0.377
  2. Developing a clinical and PET/CT volumetric prognostic index for risk assessment and management of NSCLC patients after initial therapy. Front Biosci (Landmark Ed). 2022 01 12; 27(1):16.
    View in: PubMed
    Score: 0.345
  3. The Relative Importance of Clinical and Socio-demographic Variables in Prognostic Prediction in Non-Small Cell Lung Cancer: A Variable Importance Approach. Med Care. 2020 05; 58(5):461-467.
    View in: PubMed
    Score: 0.306
  4. An updated and validated PET/CT volumetric prognostic index for non-small cell lung cancer. Lung Cancer. 2018 09; 123:136-141.
    View in: PubMed
    Score: 0.271
  5. Developing and validating a novel metabolic tumor volume risk stratification system for supplementing non-small cell lung cancer staging. Eur J Nucl Med Mol Imaging. 2018 11; 45(12):2079-2092.
    View in: PubMed
    Score: 0.269
  6. Risk-stratifying capacity of PET/CT metabolic tumor volume in stage IIIA non-small cell lung cancer. Eur J Nucl Med Mol Imaging. 2017 Aug; 44(8):1275-1284.
    View in: PubMed
    Score: 0.246
  7. Prognostic value of quantitative PET/CT in patients with a nonsmall cell lung cancer and another primary cancer. Nucl Med Commun. 2017 Feb; 38(2):185-192.
    View in: PubMed
    Score: 0.245
  8. Consistency of metabolic tumor volume of non-small-cell lung cancer primary tumor measured using 18F-FDG PET/CT at two different tracer uptake times. Nucl Med Commun. 2016 Jan; 37(1):50-6.
    View in: PubMed
    Score: 0.227
  9. Quantification of metabolic tumor activity and burden in patients with non-small-cell lung cancer: Is manual adjustment of semiautomatic gradient-based measurements necessary? Nucl Med Commun. 2015 Aug; 36(8):782-9.
    View in: PubMed
    Score: 0.220
  10. A new PET/CT volumetric prognostic index for non-small cell lung cancer. Lung Cancer. 2015 Jul; 89(1):43-9.
    View in: PubMed
    Score: 0.216
  11. Relationship between Overall Survival of Patients with Non-Small Cell Lung Cancer and Whole-Body Metabolic Tumor Burden Seen on Postsurgical Fluorodeoxyglucose PET Images. Radiology. 2015 Jun; 275(3):862-9.
    View in: PubMed
    Score: 0.212
  12. Prognostic value of metabolic tumor burden from (18)F-FDG PET in surgical patients with non-small-cell lung cancer. Acad Radiol. 2013 Jan; 20(1):32-40.
    View in: PubMed
    Score: 0.181
  13. Prognostic value of tumor burden measurement using the number of tumors in non-surgical patients with non-small cell lung cancer. Acta Radiol. 2012 Jun 01; 53(5):561-8.
    View in: PubMed
    Score: 0.177
  14. Independent prognostic value of whole-body metabolic tumor burden from FDG-PET in non-small cell lung cancer. Int J Comput Assist Radiol Surg. 2013 Mar; 8(2):181-91.
    View in: PubMed
    Score: 0.177
  15. Prognostic value of the quantitative metabolic volumetric measurement on 18F-FDG PET/CT in Stage IV nonsurgical small-cell lung cancer. Acad Radiol. 2012 Jan; 19(1):69-77.
    View in: PubMed
    Score: 0.172
  16. Prognostic value of metabolic tumor burden on 18F-FDG PET in nonsurgical patients with non-small cell lung cancer. Eur J Nucl Med Mol Imaging. 2012 Jan; 39(1):27-38.
    View in: PubMed
    Score: 0.169
  17. Interobserver variability among measurements of the maximum and mean standardized uptake values on (18)F-FDG PET/CT and measurements of tumor size on diagnostic CT in patients with pulmonary tumors. Acta Radiol. 2010 Sep; 51(7):782-8.
    View in: PubMed
    Score: 0.157
  18. Integrating PET and CT information to improve diagnostic accuracy for lung nodules: A semiautomatic computer-aided method. J Nucl Med. 2006 Jul; 47(7):1075-80.
    View in: PubMed
    Score: 0.029
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.